Epileptic seizure forecasting with wearable-based nocturnal sleep features.

OBJECTIVE: Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. METHODS: Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach. RESULTS: Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon. SIGNIFICANCE: Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. PLAIN LANGUAGE SUMMARY: Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Outcome of emergency neurosurgery in patients with refractory and super-refractory status epilepticus: a systematic review and individual participant data meta-analysis.

Background: Refractory (RSE) and super-refractory status epilepticus (SRSE) are serious neurological conditions requiring aggressive management. Beyond anesthetic agents, there is a lack of evidence guiding management in these patients. This systematic review and individual participant data meta-analysis (IPDMA) seeks to evaluate and compare the currently available surgical techniques for the acute treatment of RSE and SRSE. Methods: A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Individual Participant Data (PRISMA-IPD). Only patients who underwent surgery while in RSE and SRSE were included. Descriptive statistics were used to compare various subgroups. Multivariable logistic regression models were constructed to identify predictors of status epilepticus (SE) cessation, long-term overall seizure freedom, and favorable functional outcome (i.e., modified Rankin score of 0-2) at last follow-up. Results: A total of 87 studies including 161 participants were included. Resective surgery tended to achieve better SE cessation rate (93.9%) compared to non-resective techniques (83.9%), but this did not reach significance (p = 0.071). Resective techniques were also more likely to achieve seizure freedom (69.1% vs. 34.4%, p = <0.0001). Older age at SE (OR = 1.384[1.046-1.832], p = 0.023) was associated with increased likelihood of SE cessation, while longer duration of SE (OR = 0.603[0.362-1.003], p = 0.051) and new-onset seizures (OR = 0.244[0.069-0.860], p = 0.028) were associated with lower likelihood of SE cessation, but this did not reach significance for SE duration. Only shorter duration of SE prior to surgery (OR = 1.675[1.168-2.404], p = 0.0060) and immediate termination of SE (OR = 3.736 [1.323-10.548], p = 0.014) were independently associated with long-term seizure status. Rates of favorable functional outcomes (mRS of 0-2) were comparable between resective (44.4%) and non-resective (44.1%) techniques, and no independent predictors of outcome were identified. Conclusion: Our findings suggest that emergency neurosurgery may be a safe and effective alternative in patients with RSE/SRSE and may be considered earlier during the disease course. However, the current literature is limited exclusively to small case series and case reports with high risk of publication bias. Larger clinical trials assessing long-term seizure and functional outcomes are warranted to establish robust management guidelines.

Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.

Background: Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods: We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results: We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion: The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance: We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Cross-bispectrum connectivity of intracranial EEG: A novel approach to seizure onset zone localization.

Connectivity analyses of intracranial electroencephalography (iEEG) could guide surgical planning for epilepsy surgery by improving the delineation of the seizure onset zone. Traditional approaches fail to quantify important interactions between frequency components. To assess if effective connectivity based on cross-bispectrum -a measure of nonlinear multivariate cross-frequency coupling- can quantitatively identify generators of seizure activity, cross-bispectrum connectivity between channels was computed from iEEG recordings of 5 patients (34 seizures) with good postsurgical outcome. Personalized thresholds of 50% and 80% of the maximum coupling values were used to identify generating electrode channels. In all patients, outflow coupling between α (8-15 Hz) and ß (16-31 Hz) frequencies identified at least one electrode inside the resected seizure onset zone. With the 50% and 80% thresholds respectively, an average of 5 (44.7%; specificity = 82.6%) and 2 (22.5%; specificity = 99.0%) resected electrodes were correctly identified. Results show promise for the automatic identification of the seizure onset zone based on cross-bispectrum connectivity analysis.

Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography.

Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.

Anterior Cingulate Epilepsy: A Review.

SUMMARY: In this review, the semiology, and characteristics of noninvasive investigations suggestive of anterior cingulate and anterior midcingulate epilepsy are detailed by the authors. The clinical presentation is representative of a recently recognized rostrocaudal gradient of functional connectivity with seizures of the anterior cingulate cortex manifesting emotional and interoceptive aura followed by a hyperkinetic or complex motor seizures. The few reports of anterior midcingulate epilepsy show a trend toward a higher proportion of sensory auras and premotor semiology. Ictal pouting, vocalizations, and, in particular, laughter are strong indicators of epilepsy arising or spreading to this region. Although scalp EEG was traditionally thought to provide little information, the data provided in this review demonstrate that most patients will have abnormalities over the frontal or frontotemporal regions. Frontotemporal abnormalities at least interictally provide valuable information regarding lateralization. The etiology of epilepsy arising from the anterior cingulate region seems to be most frequently secondary to focal cortical dysplasia (FCD), followed by neoplasms and vascular lesions, particularly cavernomas, although one cannot rule out a publication bias. Findings of nuclear medicine imaging is seldomly reported but both positron emission tomography and ictal single-photon computed tomography can identify the generator or the network often showing abnormalities extending to the frontal regions. The few available magnetoencephalography (MEG) studies reveal mixed results, sometimes providing false lateralization of the focus. Anterior cingulate epilepsy is difficult to recognize, but the features summarized in this review should prompt suspicion in clinical practice.

Magnetic resonance-guided laser interstitial thermal therapy for drug-resistant epilepsy: A systematic review and individual participant data meta-analysis.

Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has emerged as a popular minimally invasive alternative to open resective surgery for drug-resistant epilepsy (DRE). We sought to perform a systematic review and individual participant data meta-analysis to identify independent predictors of seizure outcome and complications following MRgLITT for DRE. Eleven databases were searched from January 1, 2010 to February 6, 2021 using the terms "MR-guided ablation therapy" and "epilepsy". Multivariable mixed-effects Cox and logistic regression identified predictors of time to seizure recurrence, seizure freedom, operative complications, and postoperative neurological deficits. From 8705 citations, 46 studies reporting on 450 MRgLITT DRE patients (mean age = 29.5 ± 18.1 years, 49.6% female) were included. Median postoperative seizure freedom and follow-up duration were 15.5 and 19.0 months, respectively. Overall, 240 (57.8%) of 415 patients (excluding palliative corpus callosotomy) were seizure-free at last follow-up. Generalized seizure semiology (hazard ratio [HR] = 1.78, p = .020) and nonlesional magnetic resonance imaging (MRI) findings (HR = 1.50, p = .032) independently predicted shorter time to seizure recurrence. Cerebral cavernous malformation (CCM; odds ratio [OR] = 7.97, p < .001) and mesial temporal sclerosis/atrophy (MTS/A; OR = 2.21, p = .011) were independently associated with greater odds of seizure freedom at last follow-up. Operative complications occurred in 28 (8.5%) of 330 patients and were independently associated with extratemporal ablations (OR = 5.40, p = .012) and nonlesional MRI studies (OR = 3.25, p = .017). Postoperative neurological deficits were observed in 53 (15.1%) of 352 patients and were independently predicted by hypothalamic hamartoma etiology (OR = 5.93, p = .006) and invasive electroencephalographic monitoring (OR = 4.83, p = .003). Overall, MRgLITT is particularly effective in treating patients with well-circumscribed lesional DRE, such as CCM and MTS/A, but less effective in nonlesional cases or lesional cases with a more diffuse epileptogenic network associated with generalized seizures. This study identifies independent predictors of seizure freedom and complications following MRgLITT that may help further guide patient selection.

Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review.

INTRODUCTION: The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy. METHODS AND ANALYSIS: The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies. ETHICS AND DISSEMINATION: Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field. PROSPERO REGISTRATION NUMBER: CRD42022292261.

Epileptic Prodromes: Insights from Surveying 196 Patients and 150 Caregivers.

OBJECTIVE: Uncontrolled epilepsy creates a constant source of worry for patients and puts them at a high risk of injury. Identifying recurrent "premonitory" symptoms of seizures and using them to recalibrate seizure prediction algorithms may improve prediction performances. This study aimed to investigate patients' ability to predict oncoming seizures based on preictal symptoms. METHODS: Through an online survey, demographics and clinical characteristics (e.g., seizure frequency, epilepsy duration, and postictal symptom duration) were collected from people with epilepsy and caregivers across Canada. Respondents were asked to answer questions regarding their ability to predict seizures through warning symptoms. A total of 196 patients and 150 caregivers were included and were separated into three groups: those who reported warning symptoms within the 5 minutes preceding a seizure, prodromes (symptoms earlier than 5 minutes before seizure), and no warning symptoms. RESULTS: Overall, 12.2% of patients and 12.0% of caregivers reported predictive prodromes ranging from 5 minutes to more than 24 hours before the seizures (median of 2 hours). The most common were dizziness/vertigo (28%), mood changes (26%), and cognitive changes (21%). Statistical testing showed that respondents who reported prodromes also reported significantly longer postictal recovery periods compared to those who did not report predictive prodromes (P < 0.05). CONCLUSION: Findings suggest that patients who present predictive seizure prodromes may be characterized by longer patient-reported postictal recovery periods. Studying the correlation between seizure severity and predictability and investigating the electrical activity underlying prodromes may improve our understanding of preictal mechanisms and ability to predict seizures.

Clinical utility of intraoperative electrocorticography for epilepsy surgery: A systematic review and meta-analysis.

Despite the widespread use of intraoperative electrocorticography (iECoG) during resective epilepsy surgery, there are conflicting data on its overall efficacy and inability to predict benefit per pathology. Given the heterogeneity of iECoG use in resective epilepsy surgery, it is important to assess the utility of interictal-based iECoG. This individual patient data (IPD) meta-analysis seeks to identify the benefit of iECoG during resective epilepsy surgery in achieving seizure freedom for various pathologies. Embase, Scopus, and PubMed were searched from inception to January 31, 2021 using the following terms: "ecog", "electrocorticography", and "epilepsy". Articles were included if they reported seizure freedom at ≥12-month follow-up in cohorts with and without iECoG for epilepsy surgery. Non-English articles, noncomparative iECoG cohorts, and studies with <10% iECoG use were excluded. This meta-analysis followed the PRISMA 2020 guidelines. The primary outcome was seizure freedom at last follow-up and time to seizure recurrence, if applicable. Forest plots with random effects modeling assessed the relationship between iECoG use and seizure freedom. Cox regression of IPD was performed to identify predictors of longer duration of seizure freedom. Kaplan-Meier curves with log-rank test were created to visualize differences in time to seizure recurrence. Of 7504 articles identified, 18 were included for study-level analysis. iECoG was not associated with higher seizure freedom at the study level (relative risk = 1.09, 95% confidence interval [CI] = 0.96-1.23, p = .19, I2  = 64%), but on IPD (n = 7 studies, 231 patients) iECoG use was independently associated with more favorable seizure outcomes (hazard ratio = 0.47, 95% CI = .23-.95, p = .037). In Kaplan-Meier analysis of specific pathologies, iECoG use was significantly associated with longer seizure freedom only for focal cortical dysplasia (FCD; p < .001) etiology. Number needed to treat for iECoG was 8.8, and for iECoG in FCD it was 4.7. We show iECoG seizure freedom is not achieved uniformly across centers. iECoG is particularly beneficial for FCD etiology in improving seizure freedom.

Predictors of outcomes after surgery for medically intractable insular epilepsy: A systematic review and individual participant data meta-analysis.

Insular epilepsy (IE) is an increasingly recognized cause of drug-resistant epilepsy amenable to surgery. However, concerns of suboptimal seizure control and permanent neurological morbidity hamper widespread adoption of surgery for IE. We performed a systematic review and individual participant data meta-analysis to determine the efficacy and safety profile of surgery for IE and identify predictors of outcomes. Of 2483 unique citations, 24 retrospective studies reporting on 312 participants were eligible for inclusion. The median follow-up duration was 2.58 years (range, 0-17 years), and 206 (66.7%) patients were seizure-free at last follow-up. Younger age at surgery (≤18 years; HR = 1.70, 95% CI = 1.09-2.66, P = .022) and invasive EEG monitoring (HR = 1.97, 95% CI = 1.04-3.74, P = .039) were significantly associated with shorter time to seizure recurrence. Performing MR-guided laser ablation or radiofrequency ablation instead of open resection (OR = 2.05, 95% CI = 1.08-3.89, P = .028) was independently associated with suboptimal or poor seizure outcome (Engel II-IV) at last follow-up. Postoperative neurological complications occurred in 42.5% of patients, most commonly motor deficits (29.9%). Permanent neurological complications occurred in 7.8% of surgeries, including 5% and 1.4% rate of permanent motor deficits and dysphasia, respectively. Resection of the frontal operculum was independently associated with greater odds of motor deficits (OR = 2.75, 95% CI = 1.46-5.15, P = .002). Dominant-hemisphere resections were independently associated with dysphasia (OR = 13.09, 95% CI = 2.22-77.14, P = .005) albeit none of the observed language deficits were permanent. Surgery for IE is associated with a good efficacy/safety profile. Most patients experience seizure freedom, and neurological deficits are predominantly transient. Pediatric patients and those requiring invasive monitoring or undergoing stereotactic ablation procedures experience lower rates of seizure freedom. Transgression of the frontal operculum should be avoided if it is not deemed part of the epileptogenic zone. Well-selected candidates undergoing dominant-hemisphere resection are more likely to exhibit transient language deficits; however, the risk of permanent deficit is very low.

Sensorimotor outcomes after resection for perirolandic drug-resistant epilepsy: a systematic review and individual patient data meta-analysis.

OBJECTIVE: The prevalence of long-term postoperative sensorimotor deficits in children undergoing perirolandic resective epilepsy surgery is unclear. The risk of developing these deficits must be weighed against the potential reduction in seizure frequency after surgery. In this study, the authors investigated the prevalence of sensorimotor deficits after resective surgery at ≥ 1 year postoperatively. METHODS: A systematic review and individual patient data meta-analysis was conducted using PubMed, Embase, and Scopus databases. Subgroups of patients were identified and categorized according to their outcomes as follows: group A patients were denoted as seizure free with no postoperative sensorimotor deficits; group B patients experienced seizure recurrence with no deficit; group C patients were seizure free with deficits; and group D patients were not seizure free and with deficits. Rates of sensory deficits were examined in patients undergoing postcentral gyrus resection, and rates of motor deficits were aggregated in patients undergoing precentral gyrus resection. RESULTS: Of 797 articles resulting from the database searches, 6 articles including 164 pediatric patients at a mean age of 7.7 ± 5.2 years with resection for drug-resistant perirolandic epilepsy were included in the study. Seizure freedom was observed in 118 (72.9%) patients at a mean follow-up of 3.4 ± 1.8 years. In total, 109 (66.5%) patients did not develop sensorimotor deficits at last follow-up, while 55 (33.5%) had permanent deficits. Ten (14.3%) of 70 patients with postcentral gyrus resection had permanent sensory deficits. Of the postcentral gyrus resection patients, 41 (58.6%) patients were included in group A, 19 (27.1%) in group B, 7 (10.0%) in group C, and 3 (4.3%) in group D. Forty (37.7%) of 106 patients with precentral resections had permanent motor deficits. Of the precentral gyrus resection patients, 50 (47.2%) patients were in group A, 16 (15.1%) in group B, 24 (22.6%) in group C, and 16 (15.1%) in group D. Patients without focal cortical dysplasia were more likely to have permanent motor deficits relative to those with focal cortical dysplasia in the precentral surgery cohort (p = 0.02). CONCLUSIONS: In total, 58.6% of patients were seizure free without deficit, 27.1% were not seizure free and without deficit, 10.0% were seizure free but with deficit, and 4.3% were not seizure free and with deficit. Future studies with functional and quality-of-life data, particularly for patients who experience seizure recurrence with no deficits (as in group B in the present study) and those who are seizure free with deficits (as in group C) after treatment, are necessary to guide surgical decision-making.

Neuroimaging of Epilepsy.

PURPOSE OF REVIEW: This article provides an overview of imaging modalities, important imaging pathologies, and the role each imaging modality can play in the diagnosis, evaluation, and treatment of epilepsy, including epilepsy surgery. RECENT FINDINGS: The Harmonized Neuroimaging of Epilepsy Structural Sequences (HARNESS-MRI) protocol was proposed to standardize MRI imaging for all patients with seizures. The role of 7-Tesla MRI in finding previously occult epileptogenic lesions is under investigation, and the technique is increasingly used. Developing MRI postprocessing techniques can increase the sensitivity of MRI. Improvements in functional imaging techniques such as EEG-functional MRI (fMRI) and magnetic source imaging provide complementary methods of identifying seizure foci. New epileptogenic pathologies such as multinodular and vacuolating neuronal tumors (MVNT) are being discovered, and the importance of others, such as encephaloceles, is better appreciated. SUMMARY: Brain imaging is a critical component of the diagnosis and evaluation of patients with epilepsy. Structural imaging modalities such as MRI and CT allow for the identification of a wide variety of potentially epileptogenic lesions. For patients with drug-resistant epilepsy under consideration for resective surgery, both structural and functional neuroimaging may be needed for focus identification and surgical planning for preservation of neurologic function.

Validity and reliability of global ratings of satisfaction with epilepsy surgery.

OBJECTIVE: We aimed to assess the reliability and validity of single-item global ratings (GR) of satisfaction with epilepsy surgery. METHODS: We recruited 240 patients from four centers in Canada and Sweden who underwent epilepsy surgery ≥1 year earlier. Participants completed a validated questionnaire on satisfaction with epilepsy surgery (the ESSQ-19), plus a single-item GR of satisfaction with epilepsy surgery twice, 4-6 weeks apart. They also completed validated questionnaires on quality of life, depression, health state utilities, epilepsy severity and disability, medical treatment satisfaction and social desirability. Test-retest reliability of the GR was assessed with the intra-class correlation coefficient (ICC). Construct and criterion validity were examined with polyserial correlations between the GR measure of satisfaction and validated questionnaires and with the ESSQ-19 summary score. Non-parametric rank tests evaluated levels of satisfaction, and ROC analysis assessed the ability of GRs to distinguish among clinically different patient groups. RESULTS: Median age and time since surgery were 42 years (IQR 32-54) and 5 years (IQR 2-8), respectively. The GR demonstrated good to excellent test-retest reliability (ICC = 0.76; 95% CI 0.67-0.84) and criterion validity (0.85; 95% CI 0.81-0.89), and moderate correlations in the expected direction with instruments assessing quality of life (0.59; 95% CI 0.51-0.63), health utilities (0.55; 95% CI 0.45-0.65), disability (-0.51; 95% CI -0.41, -0.61), depression (-0.48; 95% CI -0.38, -0.58), and epilepsy severity (-0.48; 95% CI -0.38, -0.58). As expected, correlations were lower for social desirability (0.40; 95% CI 0.28-0.52) and medical treatment satisfaction (0.33; 95% CI 0.21-0.45). The GR distinguished participants who were seizure-free (AUC 0.75; 95% CI 0.67-0.82), depressed (AUC 0.75; 95% CI 0.67-0.83), and self-rated as having more severe epilepsy (AUC 0.78; 95% CI 0.71-0.85) and being more disabled (AUC 0.82; 95% CI 0.74-0.90). SIGNIFICANCE: The GR of epilepsy surgery satisfaction showed good measurement properties, distinguished among clinically different patient groups, and appears well-suited for use in clinical practice and research.

Decrease in wearable-based nocturnal sleep efficiency precedes epileptic seizures.

Introduction: While it is known that poor sleep is a seizure precipitant, this association remains poorly quantified. This study investigated whether seizures are preceded by significant changes in sleep efficiency as measured by a wearable equipped with an electrocardiogram, respiratory bands, and an accelerometer. Methods: Nocturnal recordings from 47 people with epilepsy hospitalized at our epilepsy monitoring unit were analyzed (304 nights). Sleep metrics during nights followed by epileptic seizures (24 h post-awakening) were compared to those of nights which were not. Results: Lower sleep efficiency (percentage of sleep during the night) was found in the nights preceding seizure days (p < 0.05). Each standard deviation decrease in sleep efficiency and increase in wake after sleep onset was respectively associated with a 1.25-fold (95 % CI: 1.05 to 1.42, p < 0.05) and 1.49-fold (95 % CI: 1.17 to 1.92, p < 0.01) increased odds of seizure occurrence the following day. Furthermore, nocturnal seizures were associated with significantly lower sleep efficiency and higher wake after sleep onset (p < 0.05), as well as increased odds of seizure occurrence following wake (OR: 5.86, 95 % CI: 2.99 to 11.77, p < 0.001). Discussion: Findings indicate lower sleep efficiency during nights preceding seizures, suggesting that wearable sensors could be promising tools for sleep-based seizure-day forecasting in people with epilepsy.

Multispectral intraoperative imaging for the detection of the hemodynamic response to interictal epileptiform discharges.

Interictal epileptiform discharges (IEDs) are brief neuronal discharges occurring between seizures in patients with epilepsy. The characterization of the hemodynamic response function (HRF) specific to IEDs could increase the accuracy of other functional imaging techniques to localize epileptiform activity, including functional near-infrared spectroscopy and functional magnetic resonance imaging. This study evaluated the possibility of using an intraoperative multispectral imaging system combined with electrocorticography (ECoG) to measure the average HRF associated with IEDs in eight patients. Inter-patient variability of the HRF is illustrated in terms of oxygenated hemoglobin peak latency, oxygenated hemoglobin increase/decrease following IEDs, and signal-to-noise ratio. A sub-region was identified using an unsupervised clustering algorithm in three patients that corresponded to the most active area identified by ECoG.

Recognition of Psychogenic Versus Epileptic Seizures Based on Videos.

OBJECTIVE: Ictal semiology interpretation for differentiating psychogenic nonepileptic seizures (PNESs) and epileptic seizures (ESs) is important for the institution of appropriate treatment. Our objective was to assess the ability of different health care professionals (HCPs) or students to distinguish PNES from ES based on video-recorded seizure semiology. METHODS: This study was designed following the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines. We showed in a random mix 36 videos of PNES or ES (18 each) and asked 558 participants to classify each seizure. The diagnostic accuracy of various groups of HCPs or students for PNES versus ES was assessed, as well as the effect of patient age and sex. Measures of diagnostic accuracy included sensitivity, specificity, and area under the curve (AUC). RESULTS: The descending order of diagnostic accuracy (AUC) was the following (p ≤ 0.001): (1) neurologists and epileptologists; (2) neurology residents; (3) other specialists and nurses with experience in epilepsy; and (4) undergraduate medical students. Although there was a strong trend toward statistical difference, with AUC 95% confidence intervals (CIs) that were not overlapping, between epileptologists (95% CI 93, 97) compared to neurologists (95% CI 88, 91), and neurologists compared to electroencephalography technicians (95% CI 82, 87), multiple pairwise comparisons with the conservative Tukey-Kramer honest significant difference test revealed no statistical difference (p = 0.25 and 0.1, respectively). Patient age and sex did not have an effect on diagnostic accuracy in neurology specialists. CONCLUSION: Visual recognition of PNES by HCPs or students varies overall proportionately with the level of expertise in the field of neurology/epilepsy.

Structural Connectivity Alterations in Operculo-Insular Epilepsy.

Operculo-insular epilepsy (OIE) is an under-recognized condition that can mimic temporal and extratemporal epilepsies. Previous studies have revealed structural connectivity changes in the epileptic network of focal epilepsy. However, most reports use the debated streamline-count to quantify 'connectivity strength' and rely on standard tracking algorithms. We propose a sophisticated cutting-edge method that is robust to crossing fibers, optimizes cortical coverage, and assigns an accurate microstructure-reflecting quantitative conectivity marker, namely the COMMIT (Convex Optimization Modeling for Microstructure Informed Tractography)-weight. Using our pipeline, we report the connectivity alterations in OIE. COMMIT-weighted matrices were created in all participants (nine patients with OIE, eight patients with temporal lobe epilepsy (TLE), and 22 healthy controls (HC)). In the OIE group, widespread increases in 'connectivity strength' were observed bilaterally. In OIE patients, 'hyperconnections' were observed between the insula and the pregenual cingulate gyrus (OIE group vs. HC group) and between insular subregions (OIE vs. TLE). Graph theoretic analyses revealed higher connectivity within insular subregions of OIE patients (OIE vs. TLE). We reveal, for the first time, the structural connectivity distribution in OIE. The observed pattern of connectivity in OIE likely reflects a diffuse epileptic network incorporating insular-connected regions and may represent a structural signature and diagnostic biomarker.

Predicting postoperative epilepsy surgery satisfaction in adults using the 19-item Epilepsy Surgery Satisfaction Questionnaire and machine learning.

OBJECTIVE: The 19-item Epilepsy Surgery Satisfaction Questionnaire (ESSQ-19) is a validated and reliable post hoc means of assessing patient satisfaction with epilepsy surgery. Prediction models building on these data can be used to counsel patients. METHODS: The ESSQ-19 was derived and validated on 229 patients recruited from Canada and Sweden. We isolated 201 (88%) patients with complete clinical data for this analysis. These patients were adults (≥18 years old) who underwent epilepsy surgery 1 year or more prior to answering the questionnaire. We extracted each patient's ESSQ-19 score (scale is 0-100; 100 represents complete satisfaction) and relevant clinical variables that were standardized prior to the analysis. We used machine learning (linear kernel support vector regression [SVR]) to predict satisfaction and assessed performance using the R2 calculated following threefold cross-validation. Model parameters were ranked to infer the importance of each clinical variable to overall satisfaction with epilepsy surgery. RESULTS: Median age was 41 years (interquartile range [IQR] = 32-53), and 116 (57%) were female. Median ESSQ-19 global score was 68 (IQR = 59-75), and median time from surgery was 5.4 years (IQR = 2.0-8.9). Linear kernel SVR performed well following threefold cross-validation, with an R2 of .44 (95% confidence interval = .36-.52). Increasing satisfaction was associated with postoperative self-perceived quality of life, seizure freedom, and reductions in antiseizure medications. Self-perceived epilepsy disability, age, and increasing frequency of seizures that impair awareness were associated with reduced satisfaction. SIGNIFICANCE: Machine learning applied postoperatively to the ESSQ-19 can be used to predict surgical satisfaction. This algorithm, once externally validated, can be used in clinical settings by fixing immutable clinical characteristics and adjusting hypothesized postoperative variables, to counsel patients at an individual level on how satisfied they will be with differing surgical outcomes.